The identification of neurobiological mechanisms underlying mental disorders and associated symptoms is critical for improving diagnostic ability and treatment efficacy. It is widely accepted that the prevalence of heterogeneity within current diagnostic categories hinders the identification of valid and reliable biomarkers, and is increasingly appreciated that symptoms themselves are heterogeneous. This is particularly true for anhedonia, which is a common symptom in the disorders of depression, schizophrenia, substance dependence and PTSD. Anhedonia is a multi-faceted symptom domain that is known to involve motivational, anticipatory and hedonic components. Importantly, these different aspects of anhedonia appear to have distinct neurobiological mechanisms, and improving diagnosis and treatment for this symptom cluster depends on developing tools for better assessment and classification of anhedonic subtypes. Our group has previously hypothesized that ?motivational anhedonia? may be a subtype of anhedonia with distinct neurobiology as compared to anhedonia without motivational symptoms. The focus of this F32 fellowship proposal is to isolate selective behavioral and neural markers that define the motivational anhedonia subtype. To accomplish this goal, we will (1) apply a new computational modeling framework developed by the applicant to a well- established measure of effort-based decision-making to more precisely characterize individual effort sensitivity and to identify individuals with and without motivational anhedonia; (2) use model-based analysis and functional imaging to validate a motivational anhedonia sub-domain and to identify biomarkers of motivational anhedonia; and (3) use longitudinal sampling methods (Ecological Momentary Assessment; EMA) to examine how symptoms of anhedonia contribute to engagement in effortful activity in daily life within-subjects, over time. Upon completion of the project outlined in this proposal, we will better understand the neurobiological mechanisms and symptom relationships that differentiate the motivational anhedonia subtype from both healthy controls and non-motivational anhedonia subtypes. Additionally, this fellowship will enable the candidate to learn how to integrate her computational modeling background with clinical research, functional neuroimaging, and EMA methods. If successful, this work will lead to greater diagnostic precision (i.e. symptom classification in clinical settings) and facilitate the development of personalized treatment plans for anhedonic patients.